The optimization problem, which consists of finding the best sets of parameters that optimize a given objective function, subject to restrictions, has been approached from different perspectives. One of them is represented by algorithms inspired by physical and natural systems, with variants and modifications that intensify their exploitation and exploration characteristics. One of the best-known algorithms is the genetic algorithm (GA) [1]}, which has been hybridized and adapted multiple times to optimize a broader range of problems. An example is where it is combined with neural networks to enhance the capacity to optimize complex nonlinear problems [2]}. Another example is the particle swarm optimization (PSO) algorithm [3]}, in which, unlike the GA, all particles have memory and share knowledge. Like the GA, it has been modified and improved multiple times; for example, in the study by [4]}, PSO was improved based on entropy, which reduces the swarm's randomness and increases the diversity of the population. An algorithm that can be considered a modification and hybridization of the GA and the PSO [4]} is the differential evolution (DE) algorithm, which treats individuals as strings of real numbers, making encoding and decoding for continuous problems unnecessary. In the case of the DE, we can also find multiple modifications and improvements, such as proposing a dual strategy strengthening global exploration and population diversity [6]}.
